"""
BSD 3-Clause License

Copyright (c) Soumith Chintala 2016,
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.



Copyright 2020 Huawei Technologies Co., Ltd

Licensed under the BSD 3-Clause License (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

https://spdx.org/licenses/BSD-3-Clause.html

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

Code source: https://github.com/pytorch/vision
"""
from __future__ import division, absolute_import
import torch.utils.model_zoo as model_zoo
from torch import nn

__all__ = [
    'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152',
    'resnext50_32x4d', 'resnext101_32x8d', 'resnet50_fc512'
]

model_urls = {
    'resnet18':
    'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34':
    'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
    'resnet50':
    'https://download.pytorch.org/models/resnet50-19c8e357.pth',
    'resnet101':
    'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
    'resnet152':
    'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
    'resnext50_32x4d':
    'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
    'resnext101_32x8d':
    'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
}


def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(
        in_planes,
        out_planes,
        kernel_size=3,
        stride=stride,
        padding=dilation,
        groups=groups,
        bias=False,
        dilation=dilation
    )


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(
        in_planes, out_planes, kernel_size=1, stride=stride, bias=False
    )


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(
        self,
        inplanes,
        planes,
        stride=1,
        downsample=None,
        groups=1,
        base_width=64,
        dilation=1,
        norm_layer=None
    ):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width != 64:
            raise ValueError(
                'BasicBlock only supports groups=1 and base_width=64'
            )
        if dilation > 1:
            raise NotImplementedError(
                "Dilation > 1 not supported in BasicBlock"
            )
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(
        self,
        inplanes,
        planes,
        stride=1,
        downsample=None,
        groups=1,
        base_width=64,
        dilation=1,
        norm_layer=None
    ):
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width/64.)) * groups
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = norm_layer(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class ResNet(nn.Module):
    """Residual network.
    
    Reference:
        - He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
        - Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017.

    Public keys:
        - ``resnet18``: ResNet18.
        - ``resnet34``: ResNet34.
        - ``resnet50``: ResNet50.
        - ``resnet101``: ResNet101.
        - ``resnet152``: ResNet152.
        - ``resnext50_32x4d``: ResNeXt50.
        - ``resnext101_32x8d``: ResNeXt101.
        - ``resnet50_fc512``: ResNet50 + FC.
    """

    def __init__(
        self,
        num_classes,
        loss,
        block,
        layers,
        zero_init_residual=False,
        groups=1,
        width_per_group=64,
        replace_stride_with_dilation=None,
        norm_layer=None,
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        **kwargs
    ):
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer
        self.loss = loss
        self.feature_dim = 512 * block.expansion
        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError(
                "replace_stride_with_dilation should be None "
                "or a 3-element tuple, got {}".
                format(replace_stride_with_dilation)
            )
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(
            3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False
        )
        self.bn1 = norm_layer(self.inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(
            block,
            128,
            layers[1],
            stride=2,
            dilate=replace_stride_with_dilation[0]
        )
        self.layer3 = self._make_layer(
            block,
            256,
            layers[2],
            stride=2,
            dilate=replace_stride_with_dilation[1]
        )
        self.layer4 = self._make_layer(
            block,
            512,
            layers[3],
            stride=last_stride,
            dilate=replace_stride_with_dilation[2]
        )
        self.global_avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = self._construct_fc_layer(
            fc_dims, 512 * block.expansion, dropout_p
        )
        self.classifier = nn.Linear(self.feature_dim, num_classes)

        self._init_params()

        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(
            block(
                self.inplanes, planes, stride, downsample, self.groups,
                self.base_width, previous_dilation, norm_layer
            )
        )
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(
                block(
                    self.inplanes,
                    planes,
                    groups=self.groups,
                    base_width=self.base_width,
                    dilation=self.dilation,
                    norm_layer=norm_layer
                )
            )

        return nn.Sequential(*layers)

    def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
        """Constructs fully connected layer

        Args:
            fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
            input_dim (int): input dimension
            dropout_p (float): dropout probability, if None, dropout is unused
        """
        if fc_dims is None:
            self.feature_dim = input_dim
            return None

        assert isinstance(
            fc_dims, (list, tuple)
        ), 'fc_dims must be either list or tuple, but got {}'.format(
            type(fc_dims)
        )

        layers = []
        for dim in fc_dims:
            layers.append(nn.Linear(input_dim, dim))
            layers.append(nn.BatchNorm1d(dim))
            layers.append(nn.ReLU(inplace=True))
            if dropout_p is not None:
                layers.append(nn.Dropout(p=dropout_p))
            input_dim = dim

        self.feature_dim = fc_dims[-1]

        return nn.Sequential(*layers)

    def _init_params(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(
                    m.weight, mode='fan_out', nonlinearity='relu'
                )
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm1d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)

    def featuremaps(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        return x

    def forward(self, x):
        f = self.featuremaps(x)
        v = self.global_avgpool(f)
        v = v.view(v.size(0), -1)

        if self.fc is not None:
            v = self.fc(v)

        if not self.training:
            return v

        y = self.classifier(v)

        if self.loss == 'softmax':
            return y
        elif self.loss == 'triplet':
            return y, v
        else:
            raise KeyError("Unsupported loss: {}".format(self.loss))


def init_pretrained_weights(model, model_url):
    """Initializes model with pretrained weights.
    
    Layers that don't match with pretrained layers in name or size are kept unchanged.
    """
    pretrain_dict = model_zoo.load_url(model_url)
    model_dict = model.state_dict()
    pretrain_dict = {
        k: v
        for k, v in pretrain_dict.items()
        if k in model_dict and model_dict[k].size() == v.size()
    }
    model_dict.update(pretrain_dict)
    model.load_state_dict(model_dict)


"""ResNet"""


def resnet18(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNet(
        num_classes=num_classes,
        loss=loss,
        block=BasicBlock,
        layers=[2, 2, 2, 2],
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['resnet18'])
    return model


def resnet34(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNet(
        num_classes=num_classes,
        loss=loss,
        block=BasicBlock,
        layers=[3, 4, 6, 3],
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['resnet34'])
    return model


def resnet50(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNet(
        num_classes=num_classes,
        loss=loss,
        block=Bottleneck,
        layers=[3, 4, 6, 3],
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['resnet50'])
    return model


def resnet101(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNet(
        num_classes=num_classes,
        loss=loss,
        block=Bottleneck,
        layers=[3, 4, 23, 3],
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['resnet101'])
    return model


def resnet152(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNet(
        num_classes=num_classes,
        loss=loss,
        block=Bottleneck,
        layers=[3, 8, 36, 3],
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['resnet152'])
    return model


"""ResNeXt"""


def resnext50_32x4d(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNet(
        num_classes=num_classes,
        loss=loss,
        block=Bottleneck,
        layers=[3, 4, 6, 3],
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        groups=32,
        width_per_group=4,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['resnext50_32x4d'])
    return model


def resnext101_32x8d(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNet(
        num_classes=num_classes,
        loss=loss,
        block=Bottleneck,
        layers=[3, 4, 23, 3],
        last_stride=2,
        fc_dims=None,
        dropout_p=None,
        groups=32,
        width_per_group=8,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['resnext101_32x8d'])
    return model


"""
ResNet + FC
"""


def resnet50_fc512(num_classes, loss='softmax', pretrained=True, **kwargs):
    model = ResNet(
        num_classes=num_classes,
        loss=loss,
        block=Bottleneck,
        layers=[3, 4, 6, 3],
        last_stride=1,
        fc_dims=[512],
        dropout_p=None,
        **kwargs
    )
    if pretrained:
        init_pretrained_weights(model, model_urls['resnet50'])
    return model
